AmberYifan/capsd-marin-8b-base-code_ifd_b4000_s0
The AmberYifan/capsd-marin-8b-base-code_ifd_b4000_s0 model is an 8 billion parameter language model, fine-tuned from marin-community/marin-8b-base. This model was specifically trained on the capsd_marin-8b-base-n80000-opc__mix_code_ifd_b4000_s0 dataset, indicating a specialization in code-related tasks. With a context length of 8192 tokens, it is designed for applications requiring robust code understanding and generation capabilities.
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Model Overview
AmberYifan/capsd-marin-8b-base-code_ifd_b4000_s0 is an 8 billion parameter language model, fine-tuned from the marin-community/marin-8b-base architecture. This model has been specialized through training on the capsd_marin-8b-base-n80000-opc__mix_code_ifd_b4000_s0 dataset, suggesting an optimization for code-related tasks.
Key Characteristics
- Base Model: Fine-tuned from
marin-community/marin-8b-base. - Parameter Count: 8 billion parameters.
- Context Length: Supports an 8192-token context window.
- Training Data: Specialized training on a dataset with a strong emphasis on code, indicating potential strengths in programming-related applications.
Training Details
The model underwent a single epoch of training using a learning rate of 1e-05, a total batch size of 64 (across 4 GPUs with 8 gradient accumulation steps), and an AdamW optimizer. A cosine learning rate scheduler was employed with a 0.03 warmup ratio. The training utilized Transformers 5.7.0, Pytorch 2.13.0+cu130, Datasets 4.0.0, and Tokenizers 0.22.2.
Potential Use Cases
Given its fine-tuning on a code-centric dataset, this model is likely suitable for tasks such as code generation, code completion, debugging assistance, and understanding programming logic.